






'''simple docstring'''







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
							from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result["""bs"""] ,model_result["""ss"""]							):
																				lowerCamelCase__								=	model_result["""result"""][batch_size][sequence_length]
																				self.assertIsNotNone(_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sgugger/tiny-distilbert-classification"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,only_pretrain_model=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,torchscript=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		@unittest.skipIf(torch_device == """cpu""" ,"""Cant do half precision"""							)
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,fpaa=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								# set architectures equal to `None`
								lowerCamelCase__								=	None
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result							)
								self.check_results_dict_not_empty(results.memory_train_result							)






		@unittest.skipIf(torch_device == """cpu""" ,"""Can't do half precision"""							)
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,fpaa=_lowerCAmelCase ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result							)
								self.check_results_dict_not_empty(results.memory_train_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tinier_bart"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result							)
								self.check_results_dict_not_empty(results.memory_inference_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result							)
								self.check_results_dict_not_empty(results.memory_train_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tinier_bart"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result							)
								self.check_results_dict_not_empty(results.memory_train_result							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__								=	PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,save_to_csv=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,inference_time_csv_file=os.path.join(_lowerCAmelCase ,"""inf_time.csv"""							) ,train_memory_csv_file=os.path.join(_lowerCAmelCase ,"""train_mem.csv"""							) ,inference_memory_csv_file=os.path.join(_lowerCAmelCase ,"""inf_mem.csv"""							) ,train_time_csv_file=os.path.join(_lowerCAmelCase ,"""train_time.csv"""							) ,env_info_csv_file=os.path.join(_lowerCAmelCase ,"""env.csv"""							) ,multi_process=_lowerCAmelCase ,)
														lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
														benchmark.run()
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""inf_time.csv"""							)							).exists()							)
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""train_time.csv"""							)							).exists()							)
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""inf_mem.csv"""							)							).exists()							)
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""train_mem.csv"""							)							).exists()							)
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""env.csv"""							)							).exists()							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_lowerCAmelCase							):
														self.assertTrue(hasattr(_lowerCAmelCase ,"""sequential"""							)							)
														self.assertTrue(hasattr(_lowerCAmelCase ,"""cumulative"""							)							)
														self.assertTrue(hasattr(_lowerCAmelCase ,"""current"""							)							)
														self.assertTrue(hasattr(_lowerCAmelCase ,"""total"""							)							)

								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__								=	PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,log_filename=os.path.join(_lowerCAmelCase ,"""log.txt"""							) ,log_print=_lowerCAmelCase ,trace_memory_line_by_line=_lowerCAmelCase ,multi_process=_lowerCAmelCase ,)
														lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
														lowerCamelCase__								=	benchmark.run()
														_check_summary_is_not_empty(result.inference_summary							)
														_check_summary_is_not_empty(result.train_summary							)
														self.assertTrue(Path(os.path.join(_lowerCAmelCase ,"""log.txt"""							)							).exists()							)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



